MongoDB：8,986 個職缺
瀏覽與 MongoDB 相關的開放職缺。
Software Developer
Backend Software Developer at a marketing/communications tech company, building .NET/C# microservices, APIs, and database-driven solutions for brand-audience messaging. Full SDLC ownership in a remote-first culture.
Back-end Developer Golang
Design, develop, and maintain backend services and RESTful APIs using Go, implementing business logic, database integrations, and asynchronous workflows. Core stack includes Go, PostgreSQL, MongoDB, Redis, Docker, messaging systems (Kafka/RabbitMQ/NATS), plus AI tools as a development aid.
Junior Manual QA Engineer (Web, CRM)
Junior Manual QA Engineer validating web applications, APIs, marketing logic, and A/B experiments for a global live-streaming and social networking platform serving millions of users. Works across cross-platform flows with databases, test management tools, and proxy debugging tools.
Manual QA Engineer (Android)
Manual QA engineer for a live-streaming social platform, testing Android/iOS/WEB apps and APIs using Postman, Swagger, MongoDB, and analytics tools to own end-to-end quality for a product with 400K+ MAU.
Senior Full-Stack Developer (Node.js+React)
Senior Full-Stack Developer building a cloud-based platform with Node.js and React, microservices on AWS, and MongoDB, while leveraging AI coding tools (Claude Code, Copilot) and contributing to AI-feature development (GenAI, agents, LangChain).
Middle DevOps Engineer
A Middle DevOps Engineer designing and maintaining AWS-based cloud infrastructure (ECS, Lambda, S3, Bedrock), CI/CD pipelines, Docker/Kubernetes workloads, and infrastructure-as-code with Terraform/Ansible.
Data Engineer (команда RecSys)
Build and maintain batch ETL pipelines (Airflow) and real-time stream processing (Spark Streaming, Kafka) for analytical data marts and ML models, and grow the feature store. Stack: Python, SQL, Airflow, Spark, Kafka, ClickHouse, MongoDB.
Middle QA Engineer (Backend)
Middle backend QA Engineer testing APIs and microservices (functional, integration, regression) at a streaming service. Day-to-day work centers on Postman/REST/Swagger, SQL against PostgreSQL and MongoDB, defect tracking in Jira, and CI awareness with GitLab CI.
Data Engineer - Senior 2
Senior data engineer at Cummins leading the design, development, and maintenance of a cloud-based data and analytics platform. Builds scalable data pipelines, data products, and AI/ML-ready datasets using Spark, Hadoop, SQL, Kafka, and modern ETL/ELT tools across Supply Chain, Finance, and Product domains.
Data Engineer - Senior 1
Senior Data Engineer at Cummins leading design and development of enterprise data pipelines and analytics platforms using Spark, Hadoop, Kafka, cloud data warehouses, and ETL/ELT tools, supporting analytics, AI/ML, and GenAI use cases across Supply Chain, Finance, and other domains.
Data Engineer 9
Data Engineer building and maintaining enterprise data pipelines, ETL/ELT transformations, and curated datasets across Supply Chain, Quality, Finance, and Product Lifecycle domains to power analytics, automation, and GenAI use cases using cloud big-data platforms.
Data Engineer 5
Data Engineer at Cummins building and maintaining enterprise-scale data pipelines, ETL/ELT transformations, and governed data products on cloud and Big Data platforms to support analytics, AI/ML, and GenAI use cases across Supply Chain, Quality, Finance, and Product Lifecycle domains.
Data Engineer 12
Data Engineer at Cummins building enterprise data products, pipelines, and curated datasets that support analytics, automation, and GenAI use cases across Supply Chain, Quality, Finance, and Product Lifecycle using Spark, Kafka, cloud platforms, SQL, and modern ETL/ELT tooling.
Data Engineer - Senior 6
Senior data engineer at Cummins designing and operating large-scale cloud data pipelines, data lakes, and ETL/ELT solutions using Spark, Scala/Java, Hive, HBase, Kafka, SQL, and NoSQL stores to deliver AI/ML- and analytics-ready data products across enterprise domains.
Data Engineer - Senior 4
Designs, builds, and optimizes reusable, governed enterprise data pipelines and cloud platforms for analytics, reporting, APIs, automation, and GenAI. The role spans SQL, Spark, Scala/Java, Kafka, Hadoop, ETL/ELT, data modeling, quality, lineage, and scalable data architecture.